Browsing by Author "Lin, Yi-Hao"

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A New SDN-Based Architecture and Authentication Method for Taiwan High-Speed Rail
(2017-01-01)
Lin, Yi-Hao
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Ho, Ping-Fan
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Chen, Jyh-Cheng
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Yang, Jen-Shun
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Chang, Hsien-Wen
The urgency of improving user experience has reached a critical point in the high-speed rail environment. However, there is little literature considering authentication failure problems in the high-speed rail environment. In this paper, we present a new authentication architecture for Taiwan High-Speed Rail (THSR). This new authentication architecture applies Software-Defined Network (SDN)-based principles to an outbound LTE gateway platform. We propose a post-authentication method suitable for the high-speed mobile environment, such as THSR. The proposed architecture and method can improve user experience regarding successful authentication ratio, authentication latency, and network access latency compared to the existing system.
A New SDN-Based Authentication Architecture and Method for Taiwan High Speed Rail
(2016)
Lin, Yi-Hao
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Ho, Ping-Fan
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Chen, Jyh-Cheng
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資訊工程學系
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Department of Computer Science
In this paper, we present a new authentication architecture for Taiwan High Speed Rail (THSR). The new authentication architecture applies multiple outbound LTE gateways. We also propose an SDN-based authentication method using post-authentication that is suitable for high-speed environment, including THSR. The proposed architecture and method can reduce network congestion and shorten the authentication latency compared to the existing system.
ASA: Adaptive VNF Scaling Algorithm for 5G Mobile Networks
(2018-01-01)
Ren, Yi
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Tuan Phung-Duc
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Liu, Yi-Kuan
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Chen, Jyh-Cheng
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Lin, Yi-Hao
5G mobile networks introduce Virtualized Network Functions (VNFs) to provide flexible services for incoming huge mobile data traffic. Compared with fixed capacity legacy network equipment, VNFs can be scaled in/out to adjust system capacity. However, hardware-based legacy network equipment is designed dedicatedly for its purpose so that it is more efficient in terms of unit cost. One challenge is to best use VNF resources and to balance the traffic between legacy network equipment and VNFs. To address this challenge, we first formulate the problem as a cost-performance tradeoff, where both VNF resource cost and system performance are quantified. Then, we propose an adaptive VNF scaling algorithm to balance the tradeoff. We derive the suitable VNF instances to handle data traffic with minimizing cost. Through extensive simulations, the adaptive algorithm is proven to provide good performance.
Congestion Control for Machine-Type Communications in LTE-A Networks
(2016-01-01)
Chang, Chia-Wei
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Lin, Yi-Hao
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Ren, Yi
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Chen, Jyh-Cheng
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資訊工程學系
Collecting data from a tremendous amount of Internet-of-Things (IoT) devices for next generation networks is a big challenge. A large number of devices may lead to severe congestion in Radio Access Network (RAN) and Core Network (CN). 3GPP has specified several mechanisms to handle the congestion caused by massive amounts of devices. However, detailed settings and strategies of them are not defined in the standards and are left for operators. In this paper, we propose two congestion control algorithms which efficiently reduce the congestion. Simulation results demonstrate that the proposed algorithms can achieve 20 similar to 40% improvement regarding accept ratio, overload degree and waiting time compared with those in LTE-A.
SensingGO - Toward Mobile/Cellular Data Measurement with Social and Rewarding Activities
(2018-01-01)
Lin, Yi-Hao
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Chen, Jyh-Cheng
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Lin, Chih-Yu
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Su, Bo-Yue
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Lee, Pei-Yu
Mobile Crowd Sensing (MCS) is a promising paradigm to collect large-scale network data globally. However, how to motivate people to collect and share data is a challenge. We believe the major reason why many MCS systems are not pervasive is because there are no incentives for people to use them. In this paper, we present SensingGO, a system which encourages people to keep sensing data by integrating incentive mechanisms. The sensed data are then transmitted to our backend server. The data we collected and the source code of SensingGO are open to anyone freely. We also demonstrate the analysis of real mobile data collected from SensingGO.
Transportation Type Identification by using Machine Learning Algorithms with Cellular Information
(2019-01-01)
Lin, Yi-Hao
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Chen, Jyh-Cheng
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Lin, Chih-Yu
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Su, Bo-Yue
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Lee, Pei-Yu
It is crucial for future 5G networks to intelligently understand how users move so that the networks can allocate different resources efficiently. In this paper, we try to find practical features to identify four common types of motorized transportations, including High-Speed Rail (HSR), subway, railway, and highway. We propose a system architecture that can provide accurate, real-time, and adaptive solution by using cellular information only. Because we do not use GPS as that in most of the prior studies, we can reduce energy consumption, size of log data, and computational time. Around 500-hour data are collected for performance evaluation. Experimental results confirm the effectiveness of the proposed algorithm, which can improve well-known machine learning algorithms to approximately 98% classification accuracy. The results also show that battery consumption can be reduced about 37%.